To measure and compare the performance generated of application-tailored or variations of the workflow, Banbury et al. [1]} proposed the widely-used MLPerf Tiny Benchmark Suite, illustrated in Table REF . The benchmark contains four tasks representative of a wider array of applications expected from microcontroller-class models. These include multiclass image recognition, binary image recognition, keyword spotting, and outlier detection. The benchmark suite also embraces the usage of standard datasets for each task, as well as quality target metrics and model size that new workflows should aim to achieve. Hardware metrics include the working memory requirements (SRAM), model size (flash), number of multiply and add operations (MACs), and latency. From Section  to Section , we discuss each block in the TinyML workflow, while in Section , we provide quantitative evaluation of the entire workflow based on applications in light of the benchmarks.
